Nmr based non-invasive and quantitative food attribute measurement apparatus and method

ABSTRACT

A non-invasive NMR based apparatus for measuring a food attribute (moisture, sugar content) in food products comprises a magnetic chamber, an RF pulsing device attached to the magnetic chamber, a sensor receiver, and a data processing unit in communication with the sensor receiver. The pulsing device exposes the food ingredients/snacks to an RF field and produces an NMR response signal that is detected by the sensor receiver. The data processing unit quantitatively measures a food attribute of the food product based on the NMR response signal.

The U.S. Government has rights in this invention pursuant to Agreement67685 between Battelle Memorial Institute Pacific Northwest Division andPepsiCo.

FIELD OF THE INVENTION

The present invention relates to Low Field Nuclear Magnetic Resonance(LF-NMR) and its use for non-invasive quantitative measurements ofmoisture, solids, and sugar content in food products.

PRIOR ART AND BACKGROUND OF THE INVENTION Prior Art Background

When a food snack such as potato chip is manufactured, food propertiessuch as texture, crispiness, and hardness, are dependent on raw materialcharacteristics (e.g. low solids or high solids potatoes, moisturecontent, sugar content). In particular, finished product characteristics(color, texture, flavor) are affected by raw material moisture and sugarvariability.

The crispiness, softness and/or crunchiness of a potato chip are just afew examples of food snack characteristics that make food appealing andsatisfying to consumers. Texture and flavor are some of the criteriawhich consumers use to judge the quality and freshness of many foods.When a food produces a physical sensation in the mouth (hard, soft,crisp, moist, dry), the consumer has a basis for determining the food'squality (fresh, stale, tender, ripe).

Currently, it is a major challenge to accurately measure the moisturecontent of raw materials. It is not feasible to determine the chemicalcomposition, most notably the moisture content, of a reasonable samplesize (e.g., >1 wt. %) of incoming foodstuffs or any related rawmaterials used for manufacturing processed foods. Because of thisinability to determine moisture levels, prior art systems and methodscannot operate food processing equipment at optimal efficiency.Furthermore, it is not currently practical to sort many incomingfoodstuffs based on their moisture content into separate processingbatches (e.g., sort and process all foodstuffs between 18% and 20%moisture, and between 20% and 22% moisture separately). Processingoptimization for maximum product quality is therefore not possible andthere is an ongoing need for rapid measurements on whole, raw foodstuffsin an entirely non-invasive manner.

Prior Art Food Snack Manufacturing System (0100)

As illustrated in FIG. 1, a prior art food snack manufacturing systemcomprises a series of apparatus that include a sourcing stage (0101),storage station (0102), wash/peel station (0103), slicing station(0104), frying station (0105), inspection station (0106), seasoningstation (0107), and a packaging station (0108). The food snacks, such aspotato chips, may be conveyed from station to station on a conveyor beltduring the manufacturing system.

Prior Art Food Snack Manufacturing Method (0200)

As generally shown in FIG. 2, a prior art manufacturing methodassociated with the prior art system in FIG. 1 may include the stepscomprising:

-   (1) Sourcing Food Ingredients (0201);-   Ingredients for food snacks, for example, potatoes may be sourced    from different farms. The potatoes may have different    moisture/solids content depending on the farms. There is therefore a    need for an apparatus to measure and to differentiate food    ingredients so that downstream processing may be optimized based on    the food attribute such as moisture and/or solids content. The    pricing and grading for food ingredient or raw materials may be    based on the input quality and moisture/solids percentage.    Therefore, there needs to be an apparatus to quantitatively measure    moisture/solids percentage in incoming food raw materials. There is    also a need to provide a non-invasive quantitative apparatus at the    receiving area, storage area, on the farm where the food raw    materials are grown, or at the off-site food storage locations.-   (2) Storing Food Ingredients (0202);-   When the potatoes arrive at the plant, they are examined for    quality. A quick fry may be performed in the receiving area (batch)    in some countries to check for sugars, etc. The method provides a    qualitative indicator of sugar level, but is not a good way to    control a process.-   The current method of determining moisture and solids percentage is    typically based on specific gravity measurements that are performed    on a very small percentage of incoming raw materials. This method is    prone to error, lacks statistical resolution of the entire load of    potatoes, and is sometimes time consuming.-   After inspection, the potatoes are loaded into a vertical helical    screw conveyer which allows stones to fall to the bottom and pushes    the potatoes up to a conveyer belt to the automatic peeling machine.    After they have been peeled, the potatoes are washed with cold    water. The peeling process may introduce a change of moisture or    solid content (percentage) in the potatoes. Therefore there is a    need to provide a non-invasive quantitative food attribute    (moisture/solid content) measuring apparatus in line after a unit    operation such as peeling.-   (3) Processing Food Ingredients (0203);-   The potatoes pass through a slicer that cuts them into slices. The    slices fall into a second cold-water wash that removes the starch    released when the potatoes are cut. The slices pass under air jets    that remove excess water as they flow into troughs filled with oil.    The oil temperature is kept at a certain temperature. Paddles gently    push the slices along. After emerging from the oil, the fried chips    are tumbled, and salt is sprinkled from receptacles positioned above    the chips. Moisture may be measured on these chips and a heat/mass    balance may be used to determine the energy input required to drive    the moisture down to a desired point. The manufacturing system    predicts the incoming water based on literature or measured values    for raw potatoes, and the processing conditions are adjusted to meet    a desired finished moisture content. There is a need for a more    inline method and apparatus to determine the moisture loss during    such processing.-   (4) Inspecting Food Snack for Quality (0204);

At the end of the trough, a wire mesh belt pulls out the hot chips. Asthe chips move along the mesh conveyer belt, excess oil is drained offand the chips begin to cool. They then move under an optical sorter thatpicks out any burnt slices and removes them with puffs of air. Thepotato chips are inspected for texture, flavor, and mouthfeel by using aqualitative tasting process.

-   (5) Determining if the quality is acceptable, if so, proceeding to    step (0208);-   Taste samples are made from each batch throughout the manufacturing    process, usually at a rate of once per hour. The tasters check the    chips for salt, seasoning, moisture, color, and overall flavor.    Color is compared to charts that show acceptable chip colors.    Texture is also qualitatively determined by tasters as compared to a    reference sample. There is a need for an automated in-line food    attribute measurement apparatus to provide an automatic continuous    closed loop feedback to control input parameters of the processing    step of the manufacturing process. Final food attributes such as    texture, flavor, starch content, etc. are not controlled unless the    moisture content is controlled at each stage of the unit operations.-   (6) If food quality is not acceptable, rejecting the food snack,    proceeding to step (0207);-   (7) Manually Adjusting Process Parameters and Proceeding to Step    (0203);-   The process parameters are adjusted manually. Therefore, there needs    to be an automatic feedback process that adjusts the input    parameters to adjust the output quality such as texture attributes    which include hardness, fracturability and denseness, flavor, starch    content, mouth feel.-   (8) Accepting the Food Snack (0208).

Prior Art Moisture/Solids Measurement Method

As generally shown in FIG. 3, a prior art in-line moisture/solidsmeasurement method may include the steps comprising:

-   (1) Measuring known high solid content potatoes and known low solid    content potatoes (0301);-   (2) Measuring specific gravity of the high and low solid potatoes    (0302);-   (3) Generating an empirical equation for moisture content based on    regression (0303); and-   (4) Measuring additional potatoes of unknown solid content and    inferring moisture content based on the predictive equation    formulated in step 3 (0304).

The above method for determining moisture is prone to variability.Generated data generally lacks granularity or statistical resolution,and predictions are based on a very small sample relative to the totalmaterial of interest. Therefore, there is a need for a precise andaccurate measurement method to quantitatively measure moisture and solidcontent attributes. Similarly, sugars are currently measured using lowsampling-rate wet chemistry methods that are precise and specific, butare time-consuming, tedious, and labor intensive.

BRIEF SUMMARY OF THE INVENTION

The present invention in various embodiments addresses one or more ofthe above objectives in the following manner. A non-invasive LF-NMRbased apparatus for measuring a food attribute (moisture, sugar content)in food products comprises a magnetic chamber, a pulsing device attachedto the magnetic chamber, a sensor receiver interior to the magneticchamber, and a data processing unit in communication with the sensorreceiver. The pulsing device exposes the food ingredients/snacks to anRF pulse that produces an NMR response signal detected by the sensorreceiver. The data processing unit quantitatively measures a foodattribute of the food product based on the NMR response signal. Afeedback and feedforward system and method for continuously controllingfood properties of food snacks in a manufacturing process includes anNMR based apparatus that is positioned inline with other unitoperations. A controller adjusts processing parameters to a foodprocessing unit based on the information from the NMR apparatus suchthat the final food attribute of a resultant food snack falls within anacceptable limit.

The present invention apparatus may be utilized for noninvasivelymeasuring a food attribute of a food product using a method comprised ofthe steps of:

-   a) presenting a food product on a surface;-   b) polarizing the nuclear magnetic moments inside the food product    during residence in a magnetic chamber;-   c) exposing the food product to an RF pulse;-   d) generating an NMR response signal from the food product;-   e) capturing and forwarding the NMR signal to a data processing    unit; and-   f) measuring the food attribute of the food product with the data    processing unit.

Integration of this and other preferred exemplary embodiment methods inconjunction with a variety of preferred exemplary embodiment systemsdescribed herein in anticipation by the overall scope of the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the advantages provided by the invention,reference should be made to the following detailed description togetherwith the accompanying drawings wherein:

FIG. 1 is a prior art food product manufacturing system.

FIG. 2 is a prior art food product manufacturing method.

FIG. 3 is a prior art food attribute measurement method.

FIG. 4 is an NMR based apparatus for quantitative measurement of a foodattribute according to an exemplary embodiment of the present invention.

FIG. 5 is an NMR based apparatus with a mass flow device or other typeof existing sensor for quantitative measurement of a food attributeaccording to an exemplary embodiment of the present invention.

FIG. 6 is a data processing unit according to an exemplary embodiment ofthe present invention.

FIG. 7a is a chart of a captured NMR signal from an NMR apparatusaccording to an exemplary embodiment of the present invention.

FIG. 7b is a chart of NMR predictions dry matter based on a predictionequation according to an exemplary embodiment of the present invention.

FIG. 7c is a chart of NMR predictions sugar based on a predictionequation according to an exemplary embodiment of the present invention.

FIG. 8 is an exemplary correlation of potato dry matter measured by anNMR based apparatus to dry matter measured with a Thermo-GravimetricAnalysis (TGA). NMR based predictions are formulated using potato weightand the apparatus of FIG. 5.

FIG. 9 is a general flow chart method for quantitative measurement of afood attribute with an NMR based apparatus according to an exemplaryembodiment of the present invention.

FIG. 10 is an exemplary flow chart method for the quantitativemeasurement of a food attribute using empirical predictions formulatedfrom NMR decay curves generally defined by equations (1), (3), or (4).Typical NMR decay curves from potatoes are shown in FIG. 7 and FIG. 11.

FIG. 11 is an exemplary decay curve generated from an NMR basedapparatus according to a preferred embodiment of the present invention.

FIG. 12 is an exemplary quantitative food attribute combined feedbackand feedforward manufacturing system according to a preferred embodimentof the present invention.

FIG. 13 is an exemplary quantitative food attribute combined feedbackand feedforward manufacturing method according to a preferred embodimentof the present invention.

FIG. 14 is an exemplary unbound moisture measurement method according toa preferred embodiment of the present invention.

FIG. 15 is an exemplary chart of fraction of detectable water vs drymatter in a food product according to a preferred embodiment of thepresent invention.

DESCRIPTION OF THE PRESENTLY EXEMPLARY EMBODIMENTS

While this invention is susceptible to embodiment in many differentforms, there is shown in the drawings and will herein be described indetail, a preferred embodiment of the invention with the understandingthat the present disclosure is to be considered as an exemplification ofthe principles of the invention and is not intended to limit the broadaspect of the invention to the embodiment illustrated.

The teachings of the present application will be described withparticular reference to the present exemplary embodiment, wherein theseinnovative teachings, both apparatus and method, are advantageouslyapplied to quantitative measurement of food attributes for foods andfood snacks. However, it should be understood that this embodiment isonly one example of the many advantageous uses of the innovativeteachings herein. In general, statements made in the specification ofthe present application do not necessarily limit any of the variousclaimed inventions. Moreover, some statements may apply to someinventive features but not to others.

Current objective methods to measure moisture are limited in detectingmoisture changes of a small magnitude with an acceptable degree ofaccuracy and require several measurements of the same substrate todifferentiate slightly different substrates with statisticalsignificance.

Consequently, there is a need for a non-invasive quantitative foodattribute measurement that accomplishes the following objectives:

-   Provide for quantitative analytical measurement of food attributes    such as moisture and solids content.-   Provide for non-invasive method for measuring solids and moisture    content without immersion in liquid media.-   Provide for quantitative test for moisture measurement for a whole    raw food ingredient such as a whole potato.-   Provide for quantitative measurement of moisture and solids with    greater accuracy reliability, and speed.-   Provide for a stand-alone quantitative food attribute measurement    apparatus deployed at a farm or an off-site food storage facility.-   Provide for measurement of absolute moisture content without the use    of an inferred empirical method.-   Provide for a feedforward system that predicts food snack output    attribute based on input food ingredient properties such as moisture    and solids content.-   Provide for high resolution moisture measurement with better than 5%    accuracy.-   Provide for repeatable and reproducible quantitative measurements of    food attributes.-   Provide for an automated method for measuring moisture and solids    content.-   Provide for a method of estimating sugar content.-   Provide for a method of measuring bound vs. unbound water.

While these objectives should not be understood to limit the teachingsof the present invention, in general these objectives are achieved inpart or in whole by the disclosed invention that is discussed in thefollowing sections. One skilled in the art will no doubt be able toselect aspects of the present invention as disclosed to affect anycombination of the objectives described above.

It should be noted that the terms “NMR based apparatus,” “NMRquantitative tool,” and “NMR based quantitative apparatus” as usedherein are used inter-changeably to indicate a tool used to measure afood property in a food snack or food ingredient, such as moisture andsugar content, based on an NMR signal. It should be noted that the termsas used herein “moisture content” and “moisture” is used to indicateabsolute amount of moisture in a food snack, food ingredient or a rawmaterial. It should be noted that the term as used herein “moisturepercentage” is used to indicate a percentage by weight of moisture in afood snack, food ingredient or a raw material. It should be noted thatthe terms as used herein “solids percentage”, “dry matter percentage”,“starch percentage”, and “sugars percentage” are used interchangeably toindicate a percentage by weight of content other than moisture presentin a food product, food snack, food ingredient, or a raw material. Thesugars referred to herein may be reducing sugars or total sugars.

The field of LF-NMR relaxometry is already well established foridentifying different molecular species and studying their dynamics incomplex materials. A general approach involves NMR measurements ofrelaxation times that govern the temporal behavior of NMR signal likeshown in FIG. 7 and FIG. 11. Results are of general interest because thestrength of NMR signal gives quantitative information about the amountof detected materials, and NMR relaxation is highly sensitive to themolecular environment where NMR signal originates. The most commonlymeasured relaxation times are T1 and T2. The first defines how fast(longitudinal) magnetization approaches thermal equilibrium after asample is exposed to an external magnetic field, and the second defineshow fast magnetization decays after an applied RF field rotateslongitudinal magnetization by 90° into the transverse (detection) plane.Based on these well-known definitions, T1 and T2 are often referred toas longitudinal and transverse relaxation times. Generally, measuredvalues for T2 are sensitive to time-averaged molecular behavior, whereasT1 is sensitive to faster molecular dynamics. In both cases, measuredvalues depend on chemical species and mobility. Thus, in complexsystems, sample heterogeneity gives rise to a distribution of relaxationtimes that can be measured using either one dimensional (1D) ormultidimensional (nD) techniques. 1D measurements generally monitor NMRsignal evolution under the influence of a single relaxation mechanism(i.e. T1 or T2), whereas, signal in nD experiments evolves under two ormore. Today, these underlying principles and methods are commonlyexploited by benchtop LF-NMR systems that are widely employed fornon-destructive testing in the food, polymer, petroleum, andpharmaceutical industries. Common applications include the determinationof solid-to-liquid and oil-to-water ratios in materials as diverse asoil-bearing rock, food emulsions, and plant seeds. A major limitation isthat no commercial benchtop LF-NMR systems are large enough toaccommodate larger samples such as whole potatoes. They are also notproperly configured for conveyor integration or the routine washingneeded for maintaining industrial hygiene standards. The presentinvention generally overcomes these common obstacles to enable thebroader use of LF-NMR in the food processing industry.

One aspect of the present invention provides an analytical method toquantitatively measure the amount of moisture and/or solids in a foodsnack or its raw material. Another aspect of the present inventionincludes a method to empirically predict a food attribute based on NMRrelaxation measurements and other input from more traditional sensors.Another aspect of the present invention includes a closed loop feedbacksystem for continuously controlling output food attributes of a foodsnack such as texture, flavor, and mouthfeel in a manufacturing process.Yet another aspect of the present invention includes a closed loopfeedback system and an open loop feedforward system for continuouslycontrolling output food attributes of a food snack such as texture,flavor, and mouthfeel of a food snack in a manufacturing process.

Applicants herein have created an apparatus positioned in amanufacturing system. The apparatus comprises a magnetic chamber formagnetizing/polarizing a food snack, a pulsing tool for applying RFfields to a food snack, a sensor receiver for recording/capturing theexcited NMR signal from the food snack, and a data processing unit thatprocesses the captured NMR signal. In one embodiment, the pulsing toolis an RF generating tool that is configured to generate an RF pulse.There are a number of embodiments of this invention which fall withinthe scope of the invention in its broadest sense.

The present invention may be seen in more detail as generallyillustrated in FIG. 4, wherein an exemplary NMR based food propertymeasuring apparatus (0400) comprises a magnetic chamber (0403), a radiofrequency (RF) pulsing device (0404) that may or may not be attached tothe magnetic chamber and exposes a food product or ingredient (0402) toRF fields, a staging station that moves and positions food products(0401), a sensor receiver (0405) that captures an NMR signal (0406), anda data processing unit (0407) to process the captured NMR signal. Thesensor receiver (0405) and the pulsing device (0404) may be the same andintegrated as an RF probe. The apparatus of FIG. 4 may further beintegrated with non-NMR sensors selected from a group comprising:infrared, microwave, ultraviolet, visible light, mass, volume, ortemperature sensors. The RF probe may expose the food products to an RFpulse and also capture an NMR signal from the food products. The foodstaging station may be movable such as conveyor or a non-movablesurface. In another embodiment, objects or food may free fall throughthe NMR device. The shape of the magnetic chamber may be a hollowtubular or cylindrical form among other possible geometries. Themagnetic chamber may be designed with current carrying wire orpermanently magnetized material that is intentionally configured withgeometries to expose the food product to a static magnetic field.According to a preferred exemplary embodiment, the magnetic chambercomprises one or more regions of differing magnetic strengths. Accordingto another preferred exemplary embodiment, the magnetic chambercomprises one or more magnets. The overall apparatus may be miniaturizedto fit a footprint of a manufacturing line. According to a preferredexemplary embodiment, multiple apparatus or units may be configured in aparallel fashion to substantially increase the throughput of foodprocessing. According to another preferred exemplary embodiment,multiple units or apparatuses such as in apparatus (0400) may work inseries to characterize changes in food products during processing alonga manufacturing line.

According to a preferred exemplary embodiment, the pulsing tool is an RFgenerating unit that generates a pulsed RF field. The staging station(0401) may be a conveyor belt carrying the food snacks/ingredients, whenan input attribute such as moisture or solids content is measured in amanufacturing process on-line. For example, the staging station maycarry potatoes spaced equally or unequally as illustrated in FIG. 4. Thespeed of the conveyor may be adjusted to limit the time a food snackresides in the magnetic chamber. In one preferred embodiment, thestaging station (conveyor) may be moving at a speed in the range of 2ft/min to 100 ft/min. According to a preferred exemplary embodiment, the(polarization) time in the magnetic chamber may range from 0.3 secs to 5secs. According to a more preferred exemplary embodiment, the(polarization) time in the magnetic chamber may range from 1.5 secs to 2secs. The staging station may carry a raw food or finished food snacksuch as tortilla chips, potato chips, oat based products, corn products,non-starch-based food snacks, any starch-based finished edible foodsnacks, legumes, pulses, cut fruit, whole fruit, tubers, vegetables orseafood. The magnetic chamber is configured to polarize food productspassing within. The magnetic chamber may be composed of one or morepermanent magnets or electromagnets. When an electromagnet is used, thecurrent passing through the magnet may be measured. The measured currentcan then be used to correct for any deviation in the current flow.Sample polarization is generally determined by the amount of hydrogenatoms in the food product. The concentration of hydrogen as measured inthe captured NMR signal may be directly related to the moisture andsolid content in the food product. All the hydrogen in the food productis polarized, but hydrogen NMR signal from solids and liquids will decayat different rates to facilitate confident differentiation. According toa preferred embodiment, the signal from the hydrogen in liquids iscaptured. Another preferred embodiment captures the solid signal or bothsolid and liquid. The direction of magnetic flux from the magneticchamber may generally be transverse or parallel to the movement of thestaging station. According to a preferred exemplary embodiment, thestatic magnetic field strength may range from 1 to 6000 Gauss. Accordingto a more preferred exemplary embodiment, the magnetic strength mayrange from 70 Gauss to 3000 Gauss. The sensor device may capture signalat or near the Larmor frequencies of captured NMR signal. Therefore, theRF pulse frequency is adjusted depending on magnetic field strength. Thepulse may be centered around the Larmor frequency or offset for spatiallocalization in conjunction with magnetic field gradients (i.e.imaging). According to an exemplary embodiment, the sensor receiver(0405) may be positioned to record/capture an NMR signal (0406) from thefood snack (0402). The sensor receiver (0405) may be in communicationwith a data processing unit (DPU) (0407) via a cable or wirelesslywithout a cable. The sensing device may capture the NMR signal across awide range of time periods or from multiple distinguished sampleregions. According to a preferred exemplary embodiment a distancebetween the signal sensing device and the food product may range from0.0 inch to 2 feet. The sensor receiver may be in communication with adata processing unit. To eliminate RF interference and potential RFexposure hazards for nearby personnel, the magnetic chamber may functionas a Faraday cage, and the RF pulsing device may also be surrounded by aconducting screen.

The sensor receiver (0405) may be connected physically with a conductingcable to the DPU (0407) via an input-output module in the DPU (0407). Inan alternate arrangement, the sensor receiver (0405) may forward the NMRsignal to the input-output module in the DPU (0407) wirelessly. Thewireless protocol may use standard protocols such as LTE, 3G, 4G, WIFI,or Bluetooth. In another exemplary embodiment, the remotely located DPU(0407) may be connected to the sensing device (0405) with wired protocolsuch as Ethernet.

The NMR signal (0406) may then be captured for a period of time. The NMRsignal (0406) may be represented as NMR signal strength (a.u.) vs. time(secs) as generally illustrated in FIG. 7. According to a preferredexemplary embodiment, the NMR signal is captured for 0.01 sec to 3minutes. According to a more preferred exemplary embodiment, the NMRsignal from the food product is captured for 0.2 sec to 5 sec. Accordingto a most preferred exemplary embodiment, the NMR signal from the foodsnack is captured for about 1 sec or less.

According to a preferred exemplary embodiment, the pulsing tool directsRF towards the food snack for a pulse duration typically for a fractionof a second. According to a most preferred exemplary embodiment, thepulsing tool directs RF towards the food snack for a pulse duration of 1microsecond to 10 milliseconds. The frequency and phase of the RFpulsing tool is also adjustable to allow for precise generation andmanipulation of NMR signal. The food product remains intact after RFfields are applied and the NMR signal detected.

The exemplary NMR based apparatus illustrated in FIG. 4 may furthercomprise a mass flow device (0408) or some other type of standard sensoras illustrated in FIG. 5 (0500). If a mass flow device is employed(0408) it may be used in conjunction with apparatus (0400) to determinethe total mass of the food product. The mass flow device can bepositioned upstream, downstream, or within the NMR apparatus. Thepositioning of the mass flow device is based on the foot print and thelogistics of the unit operations. The device (0408) may also be a volumeflow device according to an alternate embodiment. The volume flow devicemay further measure a volume of the food product and a mass may beestimated based on the measured or assumed specific gravity using anycommonly available methods. Alternate embodiments might employalternative sensing technology based on infrared or near infraredspectroscopy, visible light, ultrasonics, electrical impedance and thelike.

FIG. 7a illustrates typical NMR signal captured for two potatoes withthe NMR apparatus described in FIG. 4 (0400). Decay curves (0701) and(0702) represent NMR signals from two potatoes with a high and lowsolids content respectively. The NMR signal generated from the apparatusmay be represented by an equation (1) as described below

S(t)=A1e ⁻ ^(t/B1) +A2e ⁻ ^(t/B2) +Noise.  (1)

Here, S is the NMR signal as function of time (t), A1, A2 are two signalamplitudes and B1, B2 are two (transverse) decay times for water in twodifferent micro-environments.

In practice, signal amplitudes and decay times may be determined basedon the decay curve. A1, A2, B1, B2 are positive numbers or decimals thatare easily determined by fitting measured data to equation (1). Toformulate an empirical approach for predicting food attributes, adatabase of parameters that include time constants (B1, B2) andamplitudes (A1, A2) may be generated from NMR signals captured forpotatoes or food products with known food attributes like solids ormoisture content. The captured NMR signals may be smoothed, filtered andregressed to determine the time constants and the amplitudes as afunction of known food attributes like dry matter or moisture content.The measured data can then be used to generate a linear or higher ordermodel that predicts food attributes from NMR parameters measured fromuncharacterized potatoes or food products. If a linear prediction modelis employed, estimates of food attributes like moisture or sugar maytake the following form

Food Attribute NMR Prediction=C1*A1+C2*A2+C3*B1+C4*B2   (2)

Here, C1-C4 are modeling parameters derived from multivariate linearregression analysis of the measured database. Those skilled in the artwill also recognize that Equation 2 can be readily modified to includenon-linear prediction terms. Example predictions of moisture and sugarpredictions, based on equation 2, are shown in FIGS. 7b and 7crespectively. FIG. 7b (0720) is an exemplary correlation between NMRbased predictions of potato dry matter and actual values measured withThermo-Gravimetric Analysis (TGA). Plotted predictions employ theapparatus of FIG. 4 and are formulated using only NMR parameters. Thisshould be contrasted with FIG. 8 where results are formulated usingadditional information about potato weight. FIG. 7c (0740) is anexemplary correlation between NMR based predictions of total potatosugar and actual values measured with High Performance LiquidChromatography (HPLC). Plotted predictions employ the apparatus of FIG.4 and are formulated using only NMR parameters.

More generally, multivariate (empirical) predictions might includeinformation such as potato weight when measured values are determinedfrom other sensors. The use of potato weight together with NMR isillustrated in FIG. 8. Typical embodiments are therefore not limited toa single multivariate approach but encompass all known predictionmethods as well as multiple sensor modalities that include LF-NMR. It isalso recognized that equation (1) is a simplification of NMR signal thatcan be expressed more generally as finite or continuous sum of decayingexponentials giving rise to the following equivalent signalrepresentations

S(t)=ΣA _(i) e ⁻ ^(t/Bi) +Noise,  (3) and

S(t)=∫A(B)e ⁻ ^(t/B) dB  (4)

Besides the above empirical approaches, it is also possible to implementanalytical methods. Suppose, for example, another type of NMR signalhaving a different temporal behavior is measured. With this approach,collected NMR signal S(t), as in equation (5), depends on each potato'stotal water mass (A_(total)), its longitudinal relaxation time (C5), andthe transit time through the magnet (α). Percent moisture predictionscan then be made as in equation (6). In this case, coefficients C6, andC7 may be determined based on statistical regressions and analysis ofNMR data collected using standard samples with known water content. Thecalculation of % moisture in Eq. (6) is then completed using themeasured weight (Mass) of the food product or raw ingredient. Theanalytical use of NMR therefore provides a direct measurement ofmoisture weight, whereas, the total weight of moisture and solids iscaptured using standard gravimetric methods.

S(t=0)=(1−e ^(−α/C5))(A _(total))+Noise  (5)

% Moisture NMR Prediction=(C6+C7*(A _(total)))/Mass (6)

FIG. 8 (0800) generally illustrates an exemplary correlation betweenpotato dry matter predicted by an NMR-based apparatus and dry mattermeasured using an ‘industry standard’ analytical approach calledThermo-Gravimetric Analysis (TGA). The NMR predictions on the y-axis areformulated using a linear multivariate model that is similar to equation2 but also includes potato mass. The R² of the correlation illustratedin FIG. 8 is greater than 0.95. This is a remarkable result consideringthat NMR analysis is near real-time and is performed on whole intactpotatoes. Conversely, typical TGA requires labor-intensive processing ofwhole potatoes to ensure efficient moisture evaporation, and even withthis pre-processing, subsequent TGA measurements normally take more thantwo hours to complete.

The NMR sensor system enables a measurement of potato dry matter in amore precise and accurate method as compared to measuring specificgravity and dehydrating. The fundamental disadvantage in using thespecific gravity and dehydrating method is that these methods are noteasily incorporated into an entirely non-invasive online system.Furthermore, specific gravity measurements are susceptible to the amountof entrapped air inside the potato.

The apparatus of FIG. (0500) has several advantages over the apparatusof FIG. (0400). For example, more time is generally required to measurethe NMR signal of equations (1), (3), and (4), and this can limitthroughput for empirical predictions based on data from the apparatus ofFIG. (0040). However, an initial reading for a shorter duration may beused in conjunction with equations (5) and (6) to analytically determinemoisture content when using apparatus (0500). In this case, the drymatter content may be calculated from equation (6) using the total massas measured by the mass flow device (0408). Therefore, it is notnecessary to measure the entire NMR signal decay, and because of this,sensor throughput can be dramatically increased to greater than 5000measurements an hour.

As generally illustrated in FIG. 6 (0600), a data processing unit (DPU)(0601) comprises a control unit (0620), a display unit, a processingunit and an input output module (0602). The control unit may furthercomprise a microcontroller (0607), a logic controller (0606), and anetwork controller (0605). The display unit may be connected to thecontrol unit via a host bus. The display unit may further comprise adisplay terminal (0608) that is configured to display a graphical userinterface (GUI) (0609). The GUI (0609) may be navigated with a pointingdevice or through a keyboard connected to the DPU. The GUI (0609) may beused to input parameters such as food snack specific properties, RFcapture time, conveyor speed, NMR parameters and so on.

The processing unit may include a digital signal processing unit (0603)and a statistical processing unit (0604). The digital signal processingunit (0603) may receive input from an input-output module (0602). Thestatistical processing unit (0604) may receive input from the digitalprocessing unit (0603) and further process the input. When a sensorreceiver captures an NMR signal, the signal may be forwarded to the DPU(0601) via the input-output module (0602). The NMR signal may beforwarded to the DPU (0601) with a wired or a wireless connection. Theconnection protocol and connecting conducting wires may be chosen suchthat there is minimum loss of signal and the signal to noise ratio isacceptable for further processing. A general purpose bus may carry datato and from different modules of the DPU. It should be noted that theoperation of the bus is beyond the scope of this invention.

The microcontroller (0607) may perform instructions from a memory or aROM (0610). The instruction set of the microcontroller may beimplemented to process the data of the NMR signal. A custom instructionset may also be used by the microcontroller to prioritize and expeditethe processing of the NMR signal in real time during a manufacturingoperation. The customization of the instruction set is beyond the scopeof this invention. The logic controller may perform operations such assequencing, prioritization and automation of tasks. The logic controllermay also oversee the hand shake protocol for the bus interface. Themicrocontroller may display the food attribute information on thedisplay (0608) via GUI (0609). The logic controller may furthercontinuously monitor the state of input devices and make decisions basedupon a custom program to control the state of output devices.

According to an exemplary embodiment, a feedback controller controls aninput/output controller to adjust parameters to food processing modulessuch that the resultant output properties of the food snacks from thefood processing modules fall within an acceptable range. As generallyillustrated in FIG. 6 (0600), during a manufacturing process, food snackconveyed on a belt are struck with an RF pulse from a pulsing device.The resulting NMR signal may be captured by an NMR capturing unit (0613)and forwarded to the input/output module (0602). The input/output module(0602) may further forward the NMR signal to the digital signalprocessing unit (DSP) (0603) which processes the NMR signal.

The DSP (0603) may further comprise a smoothing module, a datatransformation module, a signal to noise enhancing module and anormalization module.

According to a preferred exemplary embodiment, the signal smoothingmodule receives input from an input-output module (0602) in a dataprocessing unit and smoothens the received raw NMR signal. The datatransformation module may transform the signal using an inverse Laplacetransform method. The data is made continuous by applying a windowingfunction to the discrete data. Windowing functions that may be appliedto the discrete data may include Barlett, Blackmon, FlatTop, Hanning,Hamming, Kaiser-Bessel, Turkey and Welch windowing functions. Asmoothing window with good frequency resolution and low spectral leakagefor a random signal type may be chosen to smoothen the data. It shouldbe noted that any commonly known windowing function may be applied to araw NMR signal to smoothen and interpolate the raw NMR data.

The transformed frequency signal from the transformation module may benoisy. A signal to noise enhancement module may receive the transformedsignal from the data transform module and enhance the signal-to-noiseratio of the signal for further processing. A predictive model for eachinput attribute (moisture, solids) of a food product may be input intothe DPU (0601). The food attribute may be measured with a method asdescribed previously and in FIG. 9 (0900). The micro controller (0607)may then direct a signal to instruct the feedback controller (0611) sothat a controller to the units of a food processing unit or foodpre-processing unit adjusts input parameters of the food processing unitor food pre-processing unit. Depending on the instructions from themicrocontroller (0607), the feedback controller (0611) may communicatewith an input/output process controller (0612). The input/output processcontroller (IOC) (0612) may be a conventional process control devicesuch as PI, PID or a PD controller. Advanced process control techniquessuch as predictive controls techniques, fuzzy logic, inferentialtechniques, constant model predictive control (CMPC), multiple inputmultiple output (MIMO), single input multiple output (SIMO), singleinput single output (SISO), and supervisory control element thatultimately provides a set point may be used in conjunction with the IOC(0612). The IOC (0612) may adjust the input parameters such as inputtemperature, dwell time to food processing units such as a fryer. TheIOC (0612) may also adjust the input parameters such as slice thicknessto food pre-processing units such as a food slicer. An auxiliary inputunit (0614) may further provide information from various sensors such assensing technology based on infrared or near infrared spectroscopy,visible light, ultrasonics, electrical impedance and so on.

A statistical processing unit (SPU) (0604) may further comprise a subsetregression module. The smoothened, transformed and normalized signalfrom the digital signal processing unit (0603) is forwarded to SPU(0604) for developing coefficients with good correlation for measuring afood attribute such as moisture and solids content. An R² value greaterthan 0.7 may be considered a good correlation between the measure valuefrom the model and TGA measured number.

As generally shown in FIG. 9, an exemplary food attribute measurementmethod may be generally described in terms of the following steps:

-   (1) presenting a food product (0901);-   The food may be presented on a surface which may be moving or    stationary. The food product may be a raw potato or a bunch of    potatoes. Alternatively, the food product may be a starch based food    snack, legumes, pulses, corn, oats, cut fruits, whole fruits,    tubers, or vegetables. Otherwise, the food product may be a starch    based food snack, non-starch based food snack or seafood-   (2) polarizing the food product in a magnetic chamber (0902);-   the polarizing step may range for a period of 0.3 second to 1    minute.-   (3) exposing the food product to an RF pulse or sequence of pulses    (0903);

The duration of RF exposure may range for a period of 1 microsecond to 2seconds.

-   (4) generating an NMR response signal from the food product (0904);

The NMR signal may be detected from different locations within the foodproduct, and may exhibit different temporal behavior and informationcontent depending on the type of NMR methods employed.

-   (5) capturing and forwarding the NMR signal to a data processing    unit (0905); and-   (6) predicting the food attribute of the food product with the data    processing unit (0906).

The food attribute may be moisture content, solids content, reducingsugars, or total sugars in a food snack. A relaxation step may beincluded after the exposing step. The food attribute may be moisturepercentage, absolute moisture content, solids content, absolute solidscontent, sugar percentage or absolute sugar content. Predictions may bebased on either analytical or empirical methods.

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

The empirical approach illustrated in FIG. 10 employs the NMR decaycurve of equations (1), (3), or (4) to predict a food attribute(moisture, solids, sugars) using the following steps:

-   (1) generating NMR signal from food products with known food    attributes (moisture and sugar content) (1001);-   (2) determining coefficients and time constants for a decay curve    (1002);-   (3) generating an NMR signal for a food product with unknown food    attributes (1003);-   (4) processing and transforming the decay curve, with the DPU    (1004); and-   (5) Determine the food attribute content (moisture and sugar) of the    food product based on the coefficients determined in step 2 (1005).

This general empirical approach or method summary may be augmented bythe various elements described herein to produce a wide variety ofinvention embodiments consistent with this overall design description.This includes multi-modal sensor approaches that combine NMR data withcomplimentary information obtained using other established sensorparadigms.

FIG. 11 generally illustrates a decay curve for food product generatedwith an NMR based apparatus. The Y-axis shows an NMR signal strength (S)plotted against time (t) on the X-axis. Two decay curves, one each for asmall high solids potato (1103) and a large low solids potato (1104),are shown in FIG. 11. Each of the curves (1103, 1104) has a portion thatintercepts the Y-axis at 1102 and 1101 respectively. The intercepts(1102, 1101) at time zero of the curves generally correlate to the totalmoisture content in the measured food snack. The solids or dry mattercan be calculated based on the moisture content when the total mass ofthe snack product is known. Any food product can therefore be passedthrough an NMR apparatus and a decay curve similar to curve (1103) or(1104) may be generated. The moisture content can therefore becalculated from the curve when a suitable calibration is performed usingsamples with known moisture to relate S(t=0) to moisture weight.

As generally illustrated in FIG. 12, an exemplary food snackmanufacturing feedforward-feedback system comprises an input foodattribute NMR based measurement tool (1216) that is positioneddownstream of a food pre-processing unit (FPU) (1230) and upstream of afood processing unit (FPU) (1220). The system (1200) illustrated in FIG.12 (1200) may be used to manufacture potato chips and other generallymanufactured food products such as potato chips, tortilla chips, cornchips or any starch based food snacks. The manufacturing system maycomprise a series of inter-connected stations that include a sourcingstage (1201), a storage station (1202), wash/peel station (1203),slicing station (1204), frying station (1205), output food propertymeasurement station (1206), a seasoning station (1207), a packagingstation (1208) and a labeling station (1209). The food snacks, such aspotato chips, may be conveyed from station to station on a conveyor beltin the manufacturing system. The storage station (1202), a foodingredient pre-treatment unit, and the wash/peel station (1203) may becombined as a food preprocessing unit (1230). The food preprocessingunit (1230) may also comprise one or a combination of the storagestation (1202), a food ingredient pre-treatment unit, and the wash/peelstation (1203). It should be noted that the food preprocessing unit maycomprise other processing units ordinarily used in the food snackmanufacturing. The FPU (1220) may include one or more of the processingunits such as slicing station (1204) and frying station (1205). Theinput food attribute NMR based tool (1216) may be placed between any twostations in the manufacturing process to capture NMR signals from thepassing product after RF exposure. For example, the measurement tool(1216) may be placed in between any two stations that may includesourcing stage (1201), storage station (1202), wash/peel station (1203),slicing station (1204), and frying station (1205). The slicing station(1204) may be connected to a slicing process controller (1212) thatcontrols input parameters to the slicing station (1204) such as slicingthickness, moisture control, solids content and slicing ridges. Thefrying station (1205) may be connected to a fryer process controller(1211) that controls input parameters to the frying station (1205) suchas oil input temperature, oil output temperature, oil volume, and fryingdwell time. An in-line feedforward control with input food attributequantitative NMR based measurement tool may enable a consistentrepeatable and reproducible manufacturing output food property quality.The food properties may include solids content, moisture, density, oilcontent, slice thickness, seasoning particle size, and elements such assodium, calcium, copper, zinc, magnesium, and potassium. According to apreferred exemplary embodiment, the quantitative food attributemeasurement tool (1216) may be positioned immediately downstream of afood preprocessing unit (1230) and upstream of the FPU (1220). Accordingto a preferred exemplary embodiment, the input food attributemeasurement tool (1216) records/captures NMR signal when a pulsingdevice (RF) exposes the food ingredients from the food preprocessingunit (1230) to RF fields and processes the NMR signal to quantitativelymeasure an input food attribute. The pulsing device may strike the foodproduct and produce an NMR signal as aforementioned in FIG. 4.

The (empirical) equation (2) for NMR predicted moisture may be developedusing the method described in FIG. 10 (1000). The coefficients and timeconstants may be programmed into the measuring tool (1216) for measuringone or more input food attributes of food ingredients such as ingredientsolids content, moisture content, sugar content and model an output foodproperty attribute such as hardness, fracturability and denseness.

For example, in a potato chip manufacturing process, input ingredientssuch as potatoes may be modelled for input attributes such as inputsolids content, sugar content, moisture, density, and slice thickness.Potatoes may be procured from various farms and may possess varyingmoisture, sugar, and solid contents. The input measuring tool (1216)measures the attributes of the potatoes and programs an input controllerthat adjusts process variables to the food processing unit (1220) suchthat the output food property attribute of the produced potato chipsfalls within an acceptable limit. The input attributes may be providedto a data processing unit in an input food attribute measuring tool. Theinput attribute measurement tool (1216) may calculate an expected foodproperty attribute such as hardness from with a correlation equation.

According to a preferred exemplary embodiment, depending on the measuredinput attribute, an input controller (1222) may control the output foodproperty of a food product from the FPU (1220). The input controller(1222) may be connected to a slicing input controller and a frying inputcontroller. Typical process control equipment such as PI and PID controldevices, may be used to program the input parameters of the slicingstation (1204) and frying station (1205). For example, if the expectedoutput texture attribute based on a measured input attribute (moisture)falls outside an acceptable limit, the input controller (1222) mayprogram an input parameter or a combination of input parameters (processvariables) to the frying unit such as frying temperature or frying time.The input controller (1222) may program an input parameter to theslicing unit so that the slices are thinner or thicker depending on thecorrelation of the output attributes to the input food attributes.According to a preferred exemplary embodiment, the input food attributemeasuring tool (1216) continuously feeds input attribute information toan input controller to program input parameters to the food processingunit (1220) such that the expected output food property attribute of thefood product falls within an acceptable limit. The acceptable limit maybe determined by desired food properties in a finished food product suchas crispiness, freshness, oil content, etc. A tighter acceptable limitmay indicate a more controlled quality process. The acceptable limit mayalso be further tuned as more data is collected. Each output foodproperty attribute may have its own acceptable limits. The measured foodproperty attributes may be monitored continuously and charted forsustaining process quality control. A statistical process control chartmay be used to monitor and control a food property attribute with anupper limit and a lower limit. Any trends and outliers from thestatistical process control chart may be used to correct, adjust, anddetect potential issues with the processing units.

Furthermore, an output food property measurement tool (1206) may bepositioned downstream of food processing unit (1220). The output foodproperty measurement tool (1206) may be similar to the input foodattribute measurement tool (1216). According to a preferred exemplaryembodiment, depending on the measured output food property attribute, anoutput controller (1212) may control the output food property attributeof a food product from the FPU (1220). The output controller (1212) maybe connected to a slicing input controller and a frying inputcontroller. Typical process control equipment such as PI, PID controldevices, may be used to control the input parameters of the slicingstation (1204) and frying station (1205). For example, if a textureattribute, such as hardness, falls outside an acceptable limit, theoutput controller (1212) may adjust an input parameter to the fryingunit such as frying temperature or frying time. The output controller(1212) may adjust an input parameter to the slicing unit so that theslices are thinner or thicker depending on the correlation of the outputtexture attribute to the input parameters. According to a preferredexemplary embodiment, the input food attribute measuring tool (1206)continuously feeds back information to control input parameters to thefood processing unit (1220) such that the output food property attributeof the food product falls within an acceptable limit.

According to a preferred exemplary embodiment, the output food propertymeasurement tool may heuristically train the input measurement tool suchthat the output food property attributes of the food product from thefood processing unit is tightly controlled. The output food propertymeasurement tool (1206) may feed information to input measurement tool(1216) so that the input parameters (process variables) to the foodprocessing unit are continuously adjusted in order to tightly controlthe output food property attribute. This is especially important as newbatches of food ingredients with varying attributes are input to thefood preprocessing unit that may impact the output food property of thefood product. The continuous feedforward and feedback loop enable asubstantially tighter control on the output food property in addition tosignificant reduction of wastage due to unacceptable food property ofthe produced food product. According a preferred exemplary embodiment,the tighter control limits may be within +−20% of the output foodproperty attribute limit. According to a more preferred exemplaryembodiment, the tighter control limits may be within +−10% of the outputfood property attribute limit. According to a most preferred exemplaryembodiment, the tighter control limits may be within +−5% of the outputfood property attribute limit.

As generally shown in FIG. 13, an exemplary feedforward and feedbackmanufacturing method associated with the feedback manufacturing systemin FIG. 12 may include the steps comprising:

-   (1) Sourcing food ingredients (1301);-   The food ingredients may be potatoes that may be procured from    different sources.-   (2) pre-processing food ingredients in a food pre-processing unit    (1302);-   (3) measuring an input attribute of the food ingredients with an NMR    based measuring tool (1303);-   (4) with an NMR based measuring tool, determining if an expected    output food property attribute based on the measured input attribute    is within an acceptable limit, if not, rejecting the input food    ingredients and proceeding to step (2) (1304);-   The input attribute may be food properties such as moisture content    and solids content.-   (5) program input parameters (process variables) to a food    processing unit (1305);-   (6) processing food ingredients in a food processing unit to produce    a food product (1306);-   (7) measuring output food property attribute with an output food    property measuring tool (1307);-   (8) determining if the output food property attribute is within an    acceptable limit, if so, proceeding to step (11) (1308);-   (9) if the food property attribute is outside an acceptable limit in    step (1308), rejecting the food product (1309);-   (10) feeding back output food property attribute information to a    controller to adjust input parameters to the food processing unit,    proceeding to step (2) (1310); and-   (11) accepting the food product and proceeding to step (1) (1311).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

In some cases the NMR experiment used to generate signal in equations(1), (3), or (4) has a limited ability to detect fast decaying signalcomponents. When this is true, quickly decaying NMR signal is morecommonly detected with a different class of NMR experiments leading toequation (5). Under these circumstances it is possible to estimate theamount of fast decaying signal from the data measured with both types ofexperiments. In food products or their raw materials, such signal may becommonly attributed to bound water that is tightly associated withrelatively immobile surfaces in small pores or on large biomolecules.Practical methods for distinguishing bound water may therefore beemployed for monitoring important changes occurring at the microscopicand molecular scales. One exemplary method is described in FIG. 14 andconsists of the following steps:

-   (1) Measure total moisture content (first moisture content) in the    food product with the first NMR-based apparatus (1401);-   The first NMR-based apparatus may generate signal defined by    equation (5) and the apparatus may be the NMR-based apparatus    described in FIG. 4 (0400). In this case total moisture content is    determined from A_(total) in equation (5).-   (2) Measure the moisture content (second moisture content) in the    food product with a second NMR-based apparatus (1402);-   The second apparatus may be another NMR-based apparatus like FIG. 4    (0400) or FIG. 5 (0500), but in this case, generated NMR signal is    defined by equations (1), (3), or (4). If the apparatus of FIG. 5    (0500) is used, a mass flow device may be attached to the apparatus.    Moisture content from this second measurement is then determined    from S(t=0). More generally, the apparatus of step (1401) and step    (1402) may be the same if the food product is measured on the same    device two separate times using the NMR methods described above.    According to another preferred exemplary embodiment, the types of    signal measured with the first and second apparatus is switched to    create an opposite order.-   (3) Calculate the bound moisture content based on a difference or    ratio between the first moisture content and the second moisture    content.-   The bound moisture may be based on a difference of NMR signal    (A_(total)−S(t=0)) measured from step (1401) and step (1402).    Alternatively, a bound water fraction may be calculated as    (A_(total)−S(t=0))/A_(total). The bound (or unbound) moisture    content may then be used to correlate to several of the food    attributes in a final product such as texture, flavor, and color. It    also possible to control the food attributes in a final product by    controlling the unbound moisture. It should be noted that the terms    bound/unbound, freezeable/unfreezeable moisture content refer to a    moisture content that is bound and unbound in a food product.

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description. It is also noted thatbound and unbound water compartments may also be distinguished using asingle NMR experiment that generates signal defined by equations (1),(3), or (4), and is properly configured to detect fast decayingcomponents. Envisioned embodiments for measuring bound or unbound watertherefore includes alternative NMR approaches.

FIG. 15 (1500) generally shows a graph of how the fraction (f) ofdetectable water (1502) varies as a function of TGA dry-matter (1501).Within the context of FIG. fraction (f) may be defined asf=S(t=0)/A_(total). The fraction (f) of detectable water may thereforebe interpreted as the ratio of unbound water to total water(unbound+bound). The basic idea is that different NMR methods capturedifferent amounts of water depending on the rate of NMR relaxation (B)and experimental time-scales. Under certain conditions, methods leadingto equations (1), (3), or (4) only capture the more mobile (unbound)water; whereas, methods leading to equations (5) and (6) typicallycapture all water types independent of mobility and relaxation rate. Forexample, if the total moisture in a potato is 10 grams, a fraction (f)of 0.9 indicates that 9 grams of the total moisture is detectable(unbound) from S(t=0) and 1 gram is not detectable due to fast relaxingor bound components. The chart (1500) clearly shows that NMR methodsleading to equations (1), (3), or (4) do not always account for all ofthe moisture in the food product i.e., the fraction (f) is less than 1.The same results also show that the fraction (f) is highly predictive ofdry matter content measured with TGA. It should be understood thatpredictions of dry matter or other food attributes can be based on ahost of alternative bound/unbound water metrics, including−(A_(total)−S(t=0))/A_(total), or more elaborate analysis based on theinverse Laplace transform of equation (4). The important point is thatempirical methods exploiting bound/unbound water compartments can givehighly predictive results that only require NMR data and no additionalsensor input (i.e. without independent mass measurements).

According to another preferred exemplary embodiment the NMR signaldefined by equation (1) captures fast decay signal characteristic ofbound water and other solid compounds comprising carbohydrates, starchesand the like. According to another preferred exemplary embodiment, theNMR signal from solid and water constituents is differentiated todirectly estimate moisture and/or solids content without the need foradditional mass measurements. According to another preferred exemplaryembodiment multivariate prediction models may be created that includeNMR parameters for bound and unbound water, as well as different solidconstituents.

System Summary

The present invention system anticipates a wide variety of variations inthe basic theme of a non-invasive quantitative food attributemeasurement of a food product, the apparatus comprising: a magneticchamber; a pulsing device attached to the magnetic chamber; a sensorreceiver attached to the magnetic chamber; a data processing unit incommunication with at least the sensor receiver; wherein the pulsingdevice is configured to expose the food product to RF fields, therebyproducing an NMR response signal to be detected by the sensor receiver;wherein further the data processing unit is configured to quantitativelymeasure the food attribute of the food product based on the NMR responsesignal from the sensor receiver.

This general system summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Method Summary

The present invention method anticipates a wide variety of variations inthe basic theme of implementation, but can be generalized as anon-invasive method for measuring a food attribute of a food product,the method comprises the steps of:

-   a) presenting a food product on a surface;-   b) polarizing the food product in a magnetic chamber;-   c) exposing the food product to an RF pulse;-   d) generating an NMR response signal from the food product;-   e) capturing and forwarding the NMR signal to a data processing    unit; and-   f) predicting the food attribute with the data processing unit

Feedforward-Feedback Control System Summary

The present invention system anticipates a wide variety of variations inthe basic theme of controlling a food attribute of a food product in acontinuous manufacturing process, wherein the system comprises:

-   a food pre-processing unit;-   a food processing unit;-   a food attribute measuring tool positioned downstream from the food    pre-processing unit, wherein the food attribute measuring tool is    configured to quantitatively measure an attribute of food    ingredients that are input to the food processing unit, by    application of RF pulses on at least a portion of the food    ingredients and a sensor receiver to capture an NMR signal generated    by the applied RF; and-   a controller, the controller controlling a plurality of input    parameters to the food processing unit and the food pre-processing    unit based on the above food attribute measuring tool.

This general system summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Feedforward-Feedback Control Method Summary

The present invention method anticipates a wide variety of variations inthe basic theme of implementation, but can be generalized as afeedforward-feedback method for controlling a food attribute of a foodproduct with the above feedforward-feedback system. The method comprisesthe steps of:

-   (1) measuring an input attribute of food ingredients with a food    attribute measuring tool;-   (2) determining if the input attribute value is within an acceptable    input limit, if so, proceeding to step (4);-   (3) rejecting the food ingredients and proceeding to step (1);-   (4) programming plural input parameters to a food processing unit    based on the input attribute value;-   (5) producing food product from the food processing unit; and-   (6) measuring the food attribute and proceeding to step (1).

System/Method Variations

The present invention anticipates a wide variety of variations in thebasic theme of measuring food attributes. The examples presentedpreviously do not represent the entire scope of possible usages. Theyare meant to cite a few of many possibilities. The basic system andmethod may be augmented with a variety of ancillary embodiments,including but not limited to:

-   -   An embodiment wherein shape of the magnetic chamber is a hollow        cylinder.    -   An embodiment wherein the magnetic chamber further comprises        current carrying wire or permanently magnetized materials; the        current carrying wire or the permanently magnetized materials        are geometrically configured to expose the food product to a        static magnetic field    -   An embodiment wherein the food or food product is placed on a        stationary surface where the food or food product is exposed to        RF fields.    -   An embodiment wherein the food product is passing within the        magnetic chamber when RF fields are applied.    -   An embodiment wherein the sensing device is configured to        capture signal at or near the larmor frequency for the NMR        response signal.    -   An embodiment wherein a distance between the signal sensing        device and the food product ranges from 0.0 inch to 2 feet.    -   An embodiment wherein the food product is selected from a group        comprising: a starch based food snack, legumes, pulses, corn,        oats, cut fruits, whole fruits, tubers, and vegetables.    -   An embodiment wherein the food product is a starch based food        snack.    -   An embodiment wherein the food product is a raw potato.    -   An embodiment wherein the food product is a finished food snack.    -   An embodiment wherein the data processing unit further comprises        a digital signal processing unit and a food attribute computing        unit.    -   An embodiment wherein the food attribute is selected from a        group comprising: moisture percentage, absolute moisture        content, sugar percentage or absolute sugar content.    -   An embodiment wherein the food product remains intact after the        application of RF fields.    -   An embodiment wherein the sensor receiver is configured to be        wired to the data processing unit.    -   An embodiment wherein the sensor receiver is configured to be        wirelessly connected to the data processing unit.    -   An embodiment wherein the magnetic chamber is configured to        generate magnetic field in a range of 1 Gauss to 6000 Gauss.    -   An embodiment is further configured with a mass flow device, the        mass flow device configured to measure mass of the food product.    -   An embodiment wherein a percentage number or fraction of the        food property is calculated based on a decay curve of the NMR        signal.        One skilled in the art will recognize that other embodiments are        possible based on combinations of elements taught within the        above invention description.

1. An apparatus for non-invasive quantitative food attribute measurementof a food product, said apparatus comprising: a magnetic chamber; an RFpulsing device configured to be attached to the magnetic chamber; asensor receiver configured to be attached to the magnetic chamber; adata processing unit in communication with at least the sensor receiver;wherein the pulsing device is configured to expose the food product toRF fields, thereby producing an NMR response signal to be detected bythe sensor receiver; wherein further the data processing unit isconfigured to quantitatively measure the food attribute of the foodproduct based on the NMR response signal from the sensor receiver. 2.The apparatus of claim 1, wherein the magnetic chamber further comprisescurrent carrying wire or permanently magnetized materials; the currentcarrying wire or the permanently magnetized materials are geometricallyconfigured to expose the food product to a static magnetic field.
 3. Theapparatus of claim 1, wherein the magnetic chamber comprises one or moreregions of differing magnetic strengths.
 4. The apparatus of claim 1,wherein the food product is placed on a stationary surface when RFfields are applied to the food product.
 5. The apparatus of claim 1,wherein the food product is moving when RF fields are applied to thefood product.
 6. The apparatus of claim 1 is further integrated withnon-NMR sensors selected from a group comprising: infrared, microwave,ultraviolet, visible light, mass, volume, or temperature sensors.
 7. Theapparatus of claim 1, wherein multiple units work in parallel toincrease throughput at the same point of food processing.
 8. Theapparatus of claim 1, wherein multiple units work in series tocharacterize changes in food products during processing.
 9. Theapparatus of claim 1, wherein the sensing device is configured tocapture frequencies at or near the Larmor frequency of the NMR responsesignal.
 10. The apparatus of claim 1, wherein a distance between thesensing device and the food product ranges from 0.0 inch to 2 feet. 11.The apparatus of claim 1, wherein the food product is selected from agroup comprising: a starch based food snack, legumes, pulses, corn,oats, cut fruits, whole fruits, tubers, and vegetables.
 12. Theapparatus of claim 1, wherein the food product is at least one rawpotato.
 13. The apparatus of claim 1, wherein the food product is afinished food snack.
 14. The apparatus of claim 1, wherein the dataprocessing unit further comprises a digital signal processing unit and afood attribute computing unit.
 15. The apparatus of claim 1, wherein thefood attribute is selected from a group comprising: moisture percentage,absolute moisture content, solids percentage, absolute solids content,sugar percentage or absolute sugar content.
 16. The apparatus of claim1, wherein the food product remains intact after RF fields are applied.17. The apparatus of claim 1, wherein the food product moves during NMRsignal detection.
 18. The apparatus of claim 1 wherein the sensorreceiver is configured to be wired to the data processing unit.
 19. Theapparatus of claim 1 wherein the sensor receiver is configured to bewirelessly connected to the data processing unit.
 20. The apparatus ofclaim 1 wherein the magnetic chamber is configured to generate magneticfield in a range of 1 Gauss to 6000 Gauss.
 21. The apparatus of claim 1is further configured with a mass flow device, the mass flow deviceconfigured to measure mass of the food product.
 22. The apparatus ofclaim 1 is further configured with a volume flow device, the volume flowdevice configured to measure volume of the food product.
 23. Anon-invasive method for measuring a food attribute of a food product,the method comprises the steps of: a) presenting a food product; b)polarizing the food product in a magnetic chamber; c) exposing the foodproduct to an RF pulse; d) generating an NMR response signal from thefood product; e) capturing and forwarding the NMR signal to a dataprocessing unit; and f) predicting the food attribute with the dataprocessing unit.
 24. The method of claim 23 wherein NMR signal isdetected from different locations within the food product.
 25. Themethod of claim 23 wherein the food product is stationary when the RFpulse strikes the food product.
 26. The method of claim 23 wherein thefood product is moving when the RF pulse strikes the food product. 27.The method of claim 23 wherein RF exposure ranges for a period of 1microsecond to 60 seconds.
 28. The method of claim 23 wherein thepolarizing step ranges for a period of 0.3 second to 1 minute.
 29. Themethod of claim 23 wherein the food product is selected from a groupcomprising: a starch based food snack, non-starch based food snack orseafood.
 30. The method of claim 23 wherein the food attribute isselected from a group comprising: moisture percentage, absolute moisturecontent, solids content, absolute solids content, sugar percentage orabsolute sugar content.
 31. A method for measuring bound moisturecontent of a food product with a system comprising a first NMR basedapparatus in series with a second NMR based apparatus, wherein themethod comprises the steps of: (a) measuring a first moisture content insaid food product with the first NMR-based apparatus; (b) measuring asecond moisture content in said food product with the second NMR-basedapparatus; (c) calculating the bound moisture content based on adifference of said first moisture content and said second moisturecontent.
 32. The method of claim 31 wherein an amount of unboundmoisture is calculated based on the bound moisture.
 33. The method ofclaim 31 wherein the first NMR based apparatus and the second NMR basedapparatus are the same.
 34. The method of claim 31 wherein the types ofsignal measured with the first apparatus and the second apparatus isswitched to create an opposite order.
 35. The method of claim 31 whereinsaid step (c) further comprises calculating the bound moisture contentbased on a ratio of first moisture content and second moisture content.